propagate new changes.

src/diffusers/__init__.py

@@ -178,13 +178,12 @@ else:
         ]
     )
     _import_structure["image_processor"] = [
-        "IPAdapterMaskProcessor",
         "InpaintProcessor",
+        "IPAdapterMaskProcessor",
         "PixArtImageProcessor",
         "VaeImageProcessor",
         "VaeImageProcessorLDM3D",
     ]
-    _import_structure["video_processor"] = ["VideoProcessor"]
     _import_structure["models"].extend(
         [
             "AllegroTransformer3DModel",
@@ -396,6 +395,7 @@ else:
         ]
     )
     _import_structure["training_utils"] = ["EMAModel"]
+    _import_structure["video_processor"] = ["VideoProcessor"]
 
 try:
     if not (is_torch_available() and is_scipy_available()):

tests/models/autoencoders/test_models_autoencoder_dc.py

@@ -13,24 +13,34 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import unittest
+import pytest
+import torch
 
 from diffusers import AutoencoderDC
+from diffusers.utils.torch_utils import randn_tensor
 
-from ...testing_utils import IS_GITHUB_ACTIONS, enable_full_determinism, floats_tensor, torch_device
-from ..test_modeling_common import ModelTesterMixin
-from .testing_utils import AutoencoderTesterMixin
+from ...testing_utils import IS_GITHUB_ACTIONS, enable_full_determinism, torch_device
+from ..testing_utils import BaseModelTesterConfig, MemoryTesterMixin, ModelTesterMixin, TrainingTesterMixin
+from .testing_utils import NewAutoencoderTesterMixin
 
 
 enable_full_determinism()
 
 
-class AutoencoderDCTests(ModelTesterMixin, AutoencoderTesterMixin, unittest.TestCase):
-    model_class = AutoencoderDC
-    main_input_name = "sample"
-    base_precision = 1e-2
+class AutoencoderDCTesterConfig(BaseModelTesterConfig):
+    @property
+    def model_class(self):
+        return AutoencoderDC
 
-    def get_autoencoder_dc_config(self):
+    @property
+    def output_shape(self):
+        return (3, 32, 32)
+
+    @property
+    def generator(self):
+        return torch.Generator("cpu").manual_seed(0)
+
+    def get_init_dict(self):
         return {
             "in_channels": 3,
             "latent_channels": 4,
@@ -56,33 +66,29 @@ class AutoencoderDCTests(ModelTesterMixin, AutoencoderTesterMixin, unittest.Test
             "scaling_factor": 0.41407,
         }
 
-    @property
-    def dummy_input(self):
+    def get_dummy_inputs(self):
         batch_size = 4
         num_channels = 3
         sizes = (32, 32)
-
-        image = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
-
+        image = randn_tensor((batch_size, num_channels, *sizes), generator=self.generator, device=torch_device)
         return {"sample": image}
 
-    @property
-    def input_shape(self):
-        return (3, 32, 32)
 
-    @property
-    def output_shape(self):
-        return (3, 32, 32)
+class TestAutoencoderDC(AutoencoderDCTesterConfig, ModelTesterMixin):
+    base_precision = 1e-2
 
-    def prepare_init_args_and_inputs_for_common(self):
-        init_dict = self.get_autoencoder_dc_config()
-        inputs_dict = self.dummy_input
-        return init_dict, inputs_dict
 
-    @unittest.skipIf(IS_GITHUB_ACTIONS, reason="Skipping test inside GitHub Actions environment")
-    def test_layerwise_casting_inference(self):
-        super().test_layerwise_casting_inference()
+class TestAutoencoderDCTraining(AutoencoderDCTesterConfig, TrainingTesterMixin):
+    """Training tests for AutoencoderDC."""
 
-    @unittest.skipIf(IS_GITHUB_ACTIONS, reason="Skipping test inside GitHub Actions environment")
+
+class TestAutoencoderDCMemory(AutoencoderDCTesterConfig, MemoryTesterMixin):
+    """Memory optimization tests for AutoencoderDC."""
+
+    @pytest.mark.skipif(IS_GITHUB_ACTIONS, reason="Skipping test inside GitHub Actions environment")
     def test_layerwise_casting_memory(self):
         super().test_layerwise_casting_memory()
+
+
+class TestAutoencoderDCSlicingTiling(AutoencoderDCTesterConfig, NewAutoencoderTesterMixin):
+    """Slicing and tiling tests for AutoencoderDC."""